Core Concepts
This paper proposes a novel unsupervised deep learning framework for multi-modal groupwise image registration that leverages Bayesian inference and disentangled representation learning to estimate spatial correspondence by separating anatomical features from geometric variations.
Quotes
"This article presents a general Bayesian learning framework for multi-modal groupwise image registration."
"Remarkably, this new paradigm learns groupwise image registration in an unsupervised closed-loop self-reconstruction process, sparing the burden of designing complex image-based similarity measures."
"Our registration model, while trained using small image groups, can be readily adapted to large-scale and variable-size test groups, significantly enhancing its computational efficiency and applicability."